MaSE（Multiagent System Engineering）方法以主体理论和技术为基础,充分借鉴面向对象软件开发方法的思想,为开发分布式系统提供了较大通用性。文章研究了应用MaSE的建模技术,并采用开发工具Agent Tool构建了基于多Agent的无人机指挥...MaSE（Multiagent System Engineering）方法以主体理论和技术为基础,充分借鉴面向对象软件开发方法的思想,为开发分布式系统提供了较大通用性。文章研究了应用MaSE的建模技术,并采用开发工具Agent Tool构建了基于多Agent的无人机指挥控制系统。该系统具有自治性和协同性,从而为多无人机作战指挥控制模型建立提供了依据。展开更多
Real-time anomaly detection of massive data streams is an important research topic nowadays due to the fact that a lot of data is generated in continuous temporal processes. There is a broad research area, covering ma...Real-time anomaly detection of massive data streams is an important research topic nowadays due to the fact that a lot of data is generated in continuous temporal processes. There is a broad research area, covering mathematical, statistical, information theory methodologies for anomaly detection. It addresses various problems in a lot of domains such as health, education, finance, government, etc. In this paper, we analyze the state-of-the-art of data streams anomaly detection techniques and algorithms for anomaly detection in data streams (time series data). Critically surveying the techniques’ performances under the challenge of real-time anomaly detection of massive high-velocity streams, we conclude that the modeling of the normal behavior of the stream is a suitable approach. We evaluate Holt-Winters (HW), Taylor’s Double Holt-Winters (TDHW), Hierarchical temporal memory (HTM), Moving Average (MA), Autoregressive integrated moving average (ARIMA) forecasting models, etc. Holt-Winters (HW) and Taylor’s Double Holt-Winters (TDHW) forecasting models are used to predict the normal behavior of the periodic streams, and to detect anomalies when the deviations of observed and predicted values exceeded some predefined measures. In this work, we propose an enhancement of this approach and give a short description about the algorithms and then they are categorized by type of pre-diction as: predictive and non-predictive algorithms. We implement the Genetic Algorithm (GA) to periodically optimize HW and TDHW smoothing parameters in addition to the two sliding windows parameters that improve Hyndman’s MASE measure of deviation, and value of the threshold parameter that defines no anomaly confidence interval [1]. We also propose a new optimization function based on the input training datasets with the annotated anomaly intervals, in order to detect the right anomalies and minimize the number of false ones. The proposed method is evaluated on the known anomaly detection benchmarks NUMENTA and Yahoo datasets with a展开更多
Computational Intelligence (CI) holds the key to the development of smart grid to overcome the challenges of planning and optimization through accurate prediction of Renewable Energy Sources (RES). This paper presents...Computational Intelligence (CI) holds the key to the development of smart grid to overcome the challenges of planning and optimization through accurate prediction of Renewable Energy Sources (RES). This paper presents an architectural framework for the construction of hybrid intelligent predictor for solar power. This research investigates the applicability of heterogeneous regression algorithms for 6 hour ahead solar power availability forecasting using historical data from Rockhampton, Australia. Real life solar radiation data is collected across six years with hourly resolution from 2005 to 2010. We observe that the hybrid prediction method is suitable for a reliable smart grid energy management. Prediction reliability of the proposed hybrid prediction method is carried out in terms of prediction error performance based on statistical and graphical methods. The experimental results show that the proposed hybrid method achieved acceptable prediction accuracy. This potential hybrid model is applicable as a local predictor for any proposed hybrid method in real life application for 6 hours in advance prediction to ensure constant solar power supply in the smart grid operation.展开更多
目前智能分布式应用系统需求日益增强,对这类系统的开发是现阶段软件工程面临的一个巨大挑战。MaSE(Multiagent System Engineering)方法以主体理论和技术为基础,充分借鉴面向对象软件开发方法的思想,为开发智能分布式系统提供了较大通...目前智能分布式应用系统需求日益增强,对这类系统的开发是现阶段软件工程面临的一个巨大挑战。MaSE(Multiagent System Engineering)方法以主体理论和技术为基础,充分借鉴面向对象软件开发方法的思想,为开发智能分布式系统提供了较大通用性。分析了MaSE方法的建模技术,并把该方法应用于计算机系统安全监控软件开发中。与现有的其他软件开发方法(如结构化、面向对象)相比较,它在系统的自然建模、管理和控制系统复杂性、提高目标软件系统的灵活性、可维护性和可重用性等方面具有较大优越性。展开更多
A new oceanic general circulation model in pressure coordinates is formulated. Since the bottom pressure changes with time, the vertical coordinate is actually a pressure-sigma coordinate. The numerical solution of th...A new oceanic general circulation model in pressure coordinates is formulated. Since the bottom pressure changes with time, the vertical coordinate is actually a pressure-sigma coordinate. The numerical solution of the model is based on an energy-conservation scheme of finite difference. The most important new feature of the model is that it is a truly compressible ocean model and it is free of the Boussinesq approximations. Thus, the new model is quite different from many existing models in the following ways: 1) the exact form of mass conservation, 2) the in-situ instantaneous pressure and the UNESCO equation of state to calculate density, 3) the in-situ density in the momentum. equations, 4) finite difference schemes that conserve the total energy. Initial tests showed that the model code runs smoothly, and it is quite stable. The quasi-steady circulation patterns generated by the new model compare well with existing models, but the time evolution of the new model seems different from some existing models. Thus, the non-Boussinesq models may provide more accurate information for climate study and satellite observations.展开更多
文摘MaSE（Multiagent System Engineering）方法以主体理论和技术为基础,充分借鉴面向对象软件开发方法的思想,为开发分布式系统提供了较大通用性。文章研究了应用MaSE的建模技术,并采用开发工具Agent Tool构建了基于多Agent的无人机指挥控制系统。该系统具有自治性和协同性,从而为多无人机作战指挥控制模型建立提供了依据。
文摘Real-time anomaly detection of massive data streams is an important research topic nowadays due to the fact that a lot of data is generated in continuous temporal processes. There is a broad research area, covering mathematical, statistical, information theory methodologies for anomaly detection. It addresses various problems in a lot of domains such as health, education, finance, government, etc. In this paper, we analyze the state-of-the-art of data streams anomaly detection techniques and algorithms for anomaly detection in data streams (time series data). Critically surveying the techniques’ performances under the challenge of real-time anomaly detection of massive high-velocity streams, we conclude that the modeling of the normal behavior of the stream is a suitable approach. We evaluate Holt-Winters (HW), Taylor’s Double Holt-Winters (TDHW), Hierarchical temporal memory (HTM), Moving Average (MA), Autoregressive integrated moving average (ARIMA) forecasting models, etc. Holt-Winters (HW) and Taylor’s Double Holt-Winters (TDHW) forecasting models are used to predict the normal behavior of the periodic streams, and to detect anomalies when the deviations of observed and predicted values exceeded some predefined measures. In this work, we propose an enhancement of this approach and give a short description about the algorithms and then they are categorized by type of pre-diction as: predictive and non-predictive algorithms. We implement the Genetic Algorithm (GA) to periodically optimize HW and TDHW smoothing parameters in addition to the two sliding windows parameters that improve Hyndman’s MASE measure of deviation, and value of the threshold parameter that defines no anomaly confidence interval [1]. We also propose a new optimization function based on the input training datasets with the annotated anomaly intervals, in order to detect the right anomalies and minimize the number of false ones. The proposed method is evaluated on the known anomaly detection benchmarks NUMENTA and Yahoo datasets with a
文摘Computational Intelligence (CI) holds the key to the development of smart grid to overcome the challenges of planning and optimization through accurate prediction of Renewable Energy Sources (RES). This paper presents an architectural framework for the construction of hybrid intelligent predictor for solar power. This research investigates the applicability of heterogeneous regression algorithms for 6 hour ahead solar power availability forecasting using historical data from Rockhampton, Australia. Real life solar radiation data is collected across six years with hourly resolution from 2005 to 2010. We observe that the hybrid prediction method is suitable for a reliable smart grid energy management. Prediction reliability of the proposed hybrid prediction method is carried out in terms of prediction error performance based on statistical and graphical methods. The experimental results show that the proposed hybrid method achieved acceptable prediction accuracy. This potential hybrid model is applicable as a local predictor for any proposed hybrid method in real life application for 6 hours in advance prediction to ensure constant solar power supply in the smart grid operation.
文摘目前智能分布式应用系统需求日益增强,对这类系统的开发是现阶段软件工程面临的一个巨大挑战。MaSE(Multiagent System Engineering)方法以主体理论和技术为基础,充分借鉴面向对象软件开发方法的思想,为开发智能分布式系统提供了较大通用性。分析了MaSE方法的建模技术,并把该方法应用于计算机系统安全监控软件开发中。与现有的其他软件开发方法(如结构化、面向对象)相比较,它在系统的自然建模、管理和控制系统复杂性、提高目标软件系统的灵活性、可维护性和可重用性等方面具有较大优越性。
文摘A new oceanic general circulation model in pressure coordinates is formulated. Since the bottom pressure changes with time, the vertical coordinate is actually a pressure-sigma coordinate. The numerical solution of the model is based on an energy-conservation scheme of finite difference. The most important new feature of the model is that it is a truly compressible ocean model and it is free of the Boussinesq approximations. Thus, the new model is quite different from many existing models in the following ways: 1) the exact form of mass conservation, 2) the in-situ instantaneous pressure and the UNESCO equation of state to calculate density, 3) the in-situ density in the momentum. equations, 4) finite difference schemes that conserve the total energy. Initial tests showed that the model code runs smoothly, and it is quite stable. The quasi-steady circulation patterns generated by the new model compare well with existing models, but the time evolution of the new model seems different from some existing models. Thus, the non-Boussinesq models may provide more accurate information for climate study and satellite observations.
